Memory devices are typically provided as internal, semiconductor, integrated circuits in computers or other electronic systems. There are many different types of memory including random-access memory (RAM), read only memory (ROM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), and flash memory.
Flash memory devices have developed into a popular source of non-volatile memory for a wide range of electronic applications. Flash memory devices typically use a one-transistor memory cell that allows for high memory densities, high reliability, and low power consumption. Changes in threshold voltage of the cells, through programming of a charge storage structure, such as floating gates or trapping layers or other physical phenomena, determine the data state of each cell. Flash memory devices are commonly used in electronic systems, such as personal computers, personal digital assistants (PDAs), digital cameras, digital media players, digital recorders, games, appliances, vehicles, wireless devices, cellular telephones, and removable memory modules, and the uses for flash memory continue to expand.
Flash memory typically utilizes one of two basic architectures known as NOR flash and NAND flash. The designation is derived from the logic used to read the devices. In NOR flash architecture, a logical column of memory cells is coupled in parallel with each memory cell coupled to a data line, such as those typically referred to as digit (e.g., bit) lines. In NAND flash architecture, a column of memory cells is coupled in series with only the first memory cell of the column coupled to a bit line.
Neural networks are networks which process information by modeling a network of neurons, such as neurons in a human brain, to process information (e.g., stimuli) which has been sensed in a particular environment. Similar to a human brain, neural networks typically comprise multiple neuron models to process information. The demand for improved operating characteristics of neural networks continues to increase. Such desirable neural network operating characteristics improvements are increased speed, capacity and processing power of neural networks, for example.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for methods and devices for improving operating characteristics of neural networks.